Deep learning methods have enabled task-oriented semantic parsing of
increasingly complex utterances. However, a single model is still typically
trained and deployed for each task separately, requiring labeled training data
for each, which makes it challenging to support new tasks, even within a single
business vertical (e.g., food-ordering or travel booking). In this paper we
describe Cross-TOP (Cross-Schema Task-Oriented Parsing), a zero-shot method for
complex semantic parsing in a given vertical. By leveraging the fact that user
requests from the same vertical share lexical and semantic similarities, a
single cross-schema parser is trained to service an arbitrary number of tasks,
seen or unseen, within a vertical. We show that Cross-TOP can achieve high
accuracy on a previously unseen task without requiring any additional training
data, thereby providing a scalable way to bootstrap semantic parsers for new
tasks. As part of this work we release the FoodOrdering dataset, a
task-oriented parsing dataset in the food-ordering vertical, with utterances
and annotations derived from five schemas, each from a different restaurant
menu.

本文介绍了一种名为 Cross-TOP 的零 - shot 方法，通过利用用户请求的词汇和语义相似性，训练单个交叉架构解析器以服务于垂直内的任意数量的任务，从而为新任务引导语义解析器提供可扩展的方式。

Cross-TOP：零样本跨模式任务导向的解析

Cross-TOP: Zero-Shot Cross-Schema Task-Oriented Parsing

Task-oriented semantic parsing models have achieved strong results in recent
years, but unfortunately do not strike an appealing balance between model size,
runtime latency, and cross-domain generalizability. We tackle this problem by
introducing scenario-based semantic parsing: a variant of the original task
which first requires disambiguating an utterance's "scenario" (an intent-slot
template with variable leaf spans) before generating its frame, complete with
ontology and utterance tokens. This formulation enables us to isolate
coarse-grained and fine-grained aspects of the task, each of which we solve
with off-the-shelf neural modules, also optimizing for the axes outlined above.
Concretely, we create a Retrieve-and-Fill (RAF) architecture comprised of (1) a
retrieval module which ranks the best scenario given an utterance and (2) a
filling module which imputes spans into the scenario to create the frame. Our
model is modular, differentiable, interpretable, and allows us to garner extra
supervision from scenarios. RAF achieves strong results in high-resource,
low-resource, and multilingual settings, outperforming recent approaches by
wide margins despite, using base pre-trained encoders, small sequence lengths,
and parallel decoding.

本研究提出了基于场景的语义解析方法，通过从场景中提取粗细信息的两个神经子模块解决了任务型语义解析中的长串问题，并利用监督学习获得额外的监督信号。

针对基于场景的任务定向语义解析的检索 - 填充方法

Retrieve-and-Fill for Scenario-based Task-Oriented Semantic Parsing

Task-oriented semantic parsing models typically have high resource
requirements: to support new ontologies (i.e., intents and slots),
practitioners crowdsource thousands of samples for supervised fine-tuning.
Partly, this is due to the structure of de facto copy-generate parsers; these
models treat ontology labels as discrete entities, relying on parallel data to
extrinsically derive their meaning. In our work, we instead exploit what we
intrinsically know about ontology labels; for example, the fact that
SL:TIME_ZONE has the categorical type "slot" and language-based span "time
zone". Using this motivation, we build our approach with offline and online
stages. During preprocessing, for each ontology label, we extract its intrinsic
properties into a component, and insert each component into an inventory as a
cache of sorts. During training, we fine-tune a seq2seq, pre-trained
transformer to map utterances and inventories to frames, parse trees comprised
of utterance and ontology tokens. Our formulation encourages the model to
consider ontology labels as a union of its intrinsic properties, therefore
substantially bootstrapping learning in low-resource settings. Experiments show
our model is highly sample efficient: using a low-resource benchmark derived
from TOPv2, our inventory parser outperforms a copy-generate parser by +15 EM
absolute (44% relative) when fine-tuning on 10 samples from an unseen domain.

本研究提出了一种基于任务的语义解析模型来提高在低资源环境下的学习效率，并通过预处理和训练过程探索了本体标签的内在属性，以作为知识库，最终实现了在模型效率和性能方面的提升。

通过固有建模实现低资源任务导向语义解析

Low-Resource Task-Oriented Semantic Parsing via Intrinsic Modeling

Task-oriented semantic parsing is a critical component of virtual assistants,
which is responsible for understanding the user's intents (set reminder, play
music, etc.). Recent advances in deep learning have enabled several approaches
to successfully parse more complex queries (Gupta et al., 2018; Rongali et
al.,2020), but these models require a large amount of annotated training data
to parse queries on new domains (e.g. reminder, music).
In this paper, we focus on adapting task-oriented semantic parsers to
low-resource domains, and propose a novel method that outperforms a supervised
neural model at a 10-fold data reduction. In particular, we identify two
fundamental factors for low-resource domain adaptation: better representation
learning and better training techniques. Our representation learning uses BART
(Lewis et al., 2019) to initialize our model which outperforms encoder-only
pre-trained representations used in previous work. Furthermore, we train with
optimization-based meta-learning (Finn et al., 2017) to improve generalization
to low-resource domains. This approach significantly outperforms all baseline
methods in the experiments on a newly collected multi-domain task-oriented
semantic parsing dataset (TOPv2), which we release to the public.

本文介绍了一种新方法，使用 BART 进行表征学习并使用基于优化的元学习方法来提高到低资源域的泛化性能，以使任务导向的语义解析器适应低资源域，这种方法在新收集的多领域任务导向语义解析数据集（TOPv2）方面明显优于所有基线方法。